在 R 中使用具有相同 Id（键）列值的多行答案

【问题标题】：Working with multiple rows with same Id (key) column value in R在 R 中使用具有相同 Id（键）列值的多行
【发布时间】：2015-09-01 21:47:33
【问题描述】：

我正在处理人口普查数据。数据集的样子是：

Household-Id    Member-Type    Education    Birth
1               Father         12           1955
1               Mother         16           1963
1               Child          16           1986
1               Child          12           1995
2               Father         12           1950
2               Mother         9            1955
2               Child          18           1982
2               Child          14           1985
2               Child          16           1975
3               Father         16           1962
3               Mother         14           1965
3               Child          16           1990

我希望它看起来像：

Household-Id    Member-Type    Education    Birth    Mother-Education    Birth-Order 
1               Father         12           1955     
1               Mother         16           1963
1               Child          16           1986     16                  1
1               Child          12           1995     16                  2
2               Father         12           1950
2               Mother         9            1955
2               Child          18           1982     9                   1
2               Child          14           1985     9                   2
2               Child          16           1975     9                   3
3               Father         16           1962
3               Mother         14           1965
3               Child          16           1990     14                  1

据我所知，R 不像 Java 或 C 等语言那样支持循环操作，而且我真的不知道如何做到这一点！

【问题讨论】：

标签： r dataframe data.table dplyr

【解决方案1】：

您从哪里听说 R 不支持循环？确实如此 - 这种特殊情况听起来最适合data.table（内部使用循环）

install.packages("data.table")
library(data.table)
dat = as.data.table(YourDataFrame)

dat[Member.Type == "Child", Birth_Order:=rank(Birth) ,by=Household.Id]
dat[, MotherEducation := Education[Member.Type=="Mother"] , by=Household.Id]
dat[Member.Type != "Child", MotherEducation := NA]
dat
#   Household.Id Member.Type Education Birth MotherEducation Birth_Order
#  1:            1      Father        12  1955              NA          NA
#  2:            1      Mother        16  1963              NA          NA
#  3:            1       Child        16  1986              16           1
#  4:            1       Child        12  1995              16           2
#  5:            2      Father        12  1950              NA          NA
#  6:            2      Mother         9  1955              NA          NA
#  7:            2       Child        18  1982               9           2
#  8:            2       Child        14  1985               9           3
#  9:            2       Child        16  1975               9           1
# 10:            3      Father        16  1962              NA          NA
# 11:            3      Mother        14  1965              NA          NA
# 12:            3       Child        16  1990              14           1

【讨论】：

我是业余爱好者！谢谢。
你最后一行的:= 发生了什么？ :)

【解决方案2】：

这是dplyr 方法：

library(dplyr)

dat = dat %>% group_by(Household.Id, Member.Type) %>% 
  arrange(Birth) %>%
  mutate(Birth_Order = 1:n(),
         Birth_Order = ifelse(Member.Type=="Child", Birth_Order, NA_integer_)) %>%
  group_by(Household.Id) %>%
  mutate(Mother_Education = ifelse(Member.Type=="Child", 
                                   Education[Member.Type=="Mother"], NA))

   Household.Id Member.Type Education Birth Birth_Order Mother_Education
1             1       Child        16  1986           1               16
2             1       Child        12  1995           2               16
3             1      Father        12  1955          NA               NA
4             1      Mother        16  1963          NA               NA
5             2       Child        16  1975           1                9
6             2       Child        18  1982           2                9
7             2       Child        14  1985           3                9
8             2      Father        12  1950          NA               NA
9             2      Mother         9  1955          NA               NA
10            3       Child        16  1990           1               14
11            3      Father        16  1962          NA               NA
12            3      Mother        14  1965          NA               NA

【讨论】：